Method of processing acquired seismic data

ABSTRACT

A method of processing acquired seismic data which comprises extracting seismic information from the acquired data in a direction along the spatial direction of a body of interest thereby producing directional seismic attributes. Any preferred embodiment, the method incorporated unsupervised or supervised learning techniques.

RELATED APPLICATIONS

[0001] This application is a continuation of application Ser. No.09/786,905 filed Jun. 15, 2001, which claims the benefit of PCTApplication No. PCT/GB99/03039, filed Sep. 13, 1999 and Great BritainNo. Application 9819910.2, filed Sep. 11, 1998.

FIELD OF THE INVENTION

[0002] The present invention is concerned with a method of processingseismic signals in order to identify and characterize subsurfacefeatures within geological formations. The invention is applicable bothto onshore and offshore exploration.

BACKGROUND OF THE INVENTION

[0003] In conventional 3-D seismic surveying, seismic data is acquiredalong closely spaced lines to provide detailed subsurface information.With such high density coverage, large volumes of digital data must berecorded, stored and processed prior to interpretation. The processingrequires extensive computer resources. When the data has been processedit is interpreted in the form of a 3-D cube which effectively representsa display of subsurface features. The information within the cube can bedisplayed in various forms, such as horizontal time slice maps, verticalslices or sections in any direction.

[0004] Generally, in traditional seismic interpretation, one or moreseismic events is identified and tracked to yield a set of seismichorizons. Together these horizons are used to form the structuralframework of the subsurface in two-way time, or depth as the case maybe. All subsequent geological modeling and most of today's seismicinversion schemes rely heavily on this framework. For example, seismicattributes can be extracted around an interpreted horizon and used tocharacterize a reservoir unit.

SUMMARY OF THE INVENTION

[0005] It is an object of the present invention to provide an improvedmethod of utilizing seismic data in order to provide a more reliablemeans of detecting, separating and identifying geological features.

[0006] According to one aspect of the invention, the extraction ofseismic information from the data acquired is directed or steered alongthe object which is to be characterized.

[0007] This sense of directionality is lacking in current conventionalseismic volume interpretations; neither the direction, nor the shape ofthe bodies is utilized in the current technology. In general, in thepresent invention, the seismic volume may be converted into a domainwhere a particular geological object can be detected more easily. Forexample, shallow-gas sands may show up as bright spots in an ‘energy’attribute volume. Volume attribute transformations can be bothsingle-trace and multi-trace.

[0008] Thus directional seismic attributes are used to enhance thetexture of the objects of interest. Directional attributes are heredefined as quantities derived from a set of seismic traces along thespatial direction of the body of interest. In a subsequent step,geometrical constraints might be applied to the enhanced texture volumeto improve the detection of the geological objects of interest stillfurther.

[0009] The procedure is particularly convenient for detecting gaschimneys, but can be used equally well to detect faults, layers, and anyother type of geological objects with a spatial direction and shape. Theprocedure is also suited for the detection of reservoir changes by theuse of time lapse seismic techniques.

[0010] Conveniently, the invention provides a two step procedure aimedat detection and separation of objects from seismic data volumes. Thefirst step may enhance the texture of seismic bodies, whilepost-processing the enhanced volume is the second step. In both stepsthe spatial direction is utilized. After the enhanced volumes arerecognized they can be extracted and displayed for characterization. Theprocedure can be applied to multiple seismic volumes (reflectivity,impedance, near offset, far offset, gradient, intercept, etc.) in aniterative manner.

[0011] According to the invention, therefore, seismic attributes areextracted relative to the spatial direction of the objects which it isdesired to detect. For example, a gas chimney is basically a verticaldisturbance of the seismic response due to gas seepage. To detect suchan object, seismic data attributes would be extracted in a verticaldirection. This may be achieved e.g. by extracting attributes inmultiple time gates (actually multiple 3D control volumes) above andbelow each extraction point. Stratigraphic objects (layers, channels,sequences, etc.) and faults cannot be detected as simply as verticaldisturbances because their direction varies spatially. However, if thedominating direction in the seismic data at every sample position isknown, this direction can be used to orient the time gates or 3Dsub-volumes parallel to the direction, from which attributes areextracted. The local dominating direction, expressed as dip and azimuth,can be calculated at every seismic sample position in different ways.

[0012] The number, size and separation distance of the extractionvolumes are parameters that control the importance of spatial directionin the procedure (attribute directivity). The accuracy of the spatialdirection estimate and the attribute directivity can be tuned to preventdegradation in the attributes. According to another aspect of theinvention, therefore, the direction and shape of the control volumesfrom which attributes are extracted are adjusted to provide an optimumcombination, in dependence upon the nature of the geological featureswhich is to be detected.

[0013] Not only is the directivity of the attributes important but alsothe type and combination of attributes may be an important factor in theprocedure. Preferably, only attributes that enhance the differencebetween objects and background are elected. Multiple attributes,possibly extracted from different seismic volumes may subsequently becombined to yield optimal separation.

[0014] Hundreds of seismic attributes are nowadays available on seismicworkstations. These include the following types with potential for usein the method of the present invention:

[0015] a) seismic amplitudes at sample positions (i.e. the raw tracedata)

[0016] b) instantaneous attributes: amplitudes, phase and frequency

[0017] c) pre-stack attributes: intercept and gradient energy

[0018] d) trace to trace similarity

[0019] e) minimum and maximum amplitudes and areas

[0020] f) local dip & azimuth (used to steer the extraction volumes)

[0021] g) the number of sign changes in the derivative of the seismictraces (a new attribute).

[0022] Which of these or other attributes are chosen to enhance thetexture of an object will depend upon the nature of the objects and itsimage quality. Gas chimneys and faults for example will generallyexhibit lower trace-to-trace similarity than stratigraphic objects. Thisis because the images of faults and gas chimneys are degraded due tolimitations in acquisition and processing. Complex overburden effectsfor example, cannot be removed properly from the seismic image bycurrent processing technology. Also the spatial sampling in theacquisition pattern degrades the resolution and signal to noise ratio ofgas chimneys and faults.

[0023] In general, stratigraphic objects tend to be less degraded thanother objects. This is mainly due to the fact that seismic acquisitionand processing techniques are currently tuned to focus on horizontal andmildly dipping objects, rather than vertical, or steeply dipping events.With these considerations in mind it is logical to use trace-to-tracesimilarity as one of the attributes to enhance the difference betweengas chimneys (or faults) and their surroundings. Other attributes withseparation power could be ‘energy’ and ‘instantaneous frequency’.

[0024] In general, the selection of attributes would be based on a studyof the object and its characteristics and/or by an evaluation of theseparation strength in the attribute control volumes and/or acombination of these. Each attribute in itself has separation power butmaximum separation may be achieved by optimally combining the total setof attributes.

[0025] According to another aspect of the invention, a method of mappinga fault comprises extracting seismic information from data acquiredusing generally vertically oriented seismic control volumes sequentiallyin the region of the fault.

[0026] According to a further aspect of the invention, a method ofmapping a gas chimney or other gas formation comprises extractingseismic information from data acquired using generally verticallyoriented seismic control volumes sequentially in the region of thesurfaces of the chimney.

[0027] According to a still further aspect of the invention, a method ofmapping a stratum or layer comprises extracting seismic information fromdata acquired using sequential seismic control volumes orientedgenerally along the main spatial direction of the stratum or layer.

[0028] Attributes are preferably combined in an intelligent way toenhance the difference between bodies and background. Supervisedlearning approaches can be used for this purpose. A supervised learningapproach requires a representative set of examples to train an algorithme.g. an artificial neural network. In this case the seismic interpretermust identify a set of points in a control volume representative ofbodies and background. At these points the directional attributes ofchoice are extracted and given to the algorithm. The algorithm thenlearns how the attributes must be combined such that an optimalclassification into bodies and background is achieved. The trainedalgorithm is subsequently applied to the seismic volume(s). At everysample position the directional attributes are extracted and given tothe trained algorithm. The output is then a classification in terms ofbodies and background.

[0029] An alternative way of combining directional attributes would beto use an unsupervised learning approach. In unsupervised learning, theinternal structure of the data is sought. The algorithm, e.g. anUnsupervised Vector Quantiser (UVQ) type of neural network, segments orclusters the dataset into a number of segments. Each segment representsa certain combination of attributes. The geological significance of thesegments then remains to be interpreted.

[0030] The output of the first of the two preferred steps is a textureenhanced seismic volume. This can be a single directional attribute or avolume based on a combination of directional attributes. These volumescan be used for interpretation. Several post-processing options arefeasible to enhance the separation power, in the second step.

[0031] In the first step, only directivity is used to enhance thetexture of the object. According to a further aspect of the invention,in a second step, geometrical constraints, such as shape and dimensionof the bodies, can be applied to enhance further the separation betweenreal objects and events with similar texture. Spatial filters are oneway of increasing the signal to noise ratio. In the present invention,preferably, the local direction (dip and azimuth) at every seismicsample position are used to adapt the orientation of the spatial filter.

[0032] Another possibility to utilize existing knowledge about bodyshapes and dimensions is to employ again neural network technology or asimilar technique based on supervised learning. The network can betrained to recognize specific shapes from a subset of data containingbodies to be detected. A catalogue of examples can be constructed tocarry over knowledge from one dataset to the next. As with the spatialfilter design, the local direction at every sample position ispreferably used when the trained network is applied.

[0033] The final output of such geometrical constraints processing is anobject enhanced volume.

[0034] Edge detection algorithms are routinely used in image processingto establish the boundaries of bodies with similar characteristics. Suchalgorithms can be applied to both texture enhanced volumes and objectenhanced volumes. Edge detection algorithms applied to volumes withenhanced stratigraphic bodies provide an alternative to auto tracking ofevents in conventional seismic interpretation. (Within extractedvolumes, the horizon can be “tracked” simply by defining the horizon tofollow a seismic event, such as maximum value, a zero crossing, etc.)The boundaries can also be used as constraints for conventional autotracking algorithms. By the application of edge detection algorithms tovolumes with enhanced faults, the fault planes can also be mapped. Themethod of the present invention also provides for the tracking ofseveral horizons simultaneously.

[0035] The output of edge detection algorithms are co-ordinates of thebody boundaries. Any data from any step in the entire process accordingto the invention within these boundaries can be output for display andcharacterization purposes. For example, some directional attributesextracted from the volumes may show unique patterns that can be used totie geological units across faults. By visual—and/or neural networkbased inspection of individual bodies, the structural and stratigraphicinterpretation of a.o. layers, faults and gas chimneys can be finallydetermined.

[0036] After a set of volumes has been processed/interpreted, it may beattractive to repeat the process using knowledge gained from previousruns, or by simply focusing on special objects, regions, etc. Thus itmay be desirable to recalculate attributes in selected bodies. Thisprocedure is quite similar to generating horizon consistent attributemaps, a standard function on conventional interpretation workstations.

[0037] This form of processing/interpretation in an iterative manner hasalso great potential for time lapse seismic monitoring of, for example,reservoirs. Due to its very nature, time lapse seismic monitoring is arepetitive process aimed at detecting differences between volumes. Ingeneral, the volumes are recorded at regular time intervals and thedifferences which are to be detected are due to dynamic changes in areservoir. Examples of these changes are fluid movements, pressurechanges, temperature changes, etc. Such differences have a direction,shape and dimension. In other words they are seismic bodies, that can bedetected and separated by the method according to the present invention.

[0038] An important issue in the context of time lapse seismicmonitoring is repeatability. Seismic acquisition parameters, surveyparameters, environmental influences and seismic processing may varybetween successive recordings. This implies that small reservoir changesmay be virtually impossible to detect. To improve repeatability may bevery costly, or even impossible using current technology. However, themethod of the present invention is expected to be able to cope with thisproblem more effectively than conventional methods for two reasons.

[0039] Firstly, the knowledge of directivity is used to increasedetectability of changes between successive recordings and associateddifference volumes. Secondly, supervised learning methods such as neuralnetworks are employed, which in general perform better than conventionaltechniques on noise contaminated data. Moreover, these techniques can beused to remove the unwanted non-repeatable noise by means of aÔmatchingÕ process. A network can be trained to predict the seismicresponse of the successive recording whereby the training set isconstructed from data points outside the area where changes are to beexpected.

[0040] The present invention is particularly suited to the treatment ofchimney cubes. In a preferred variant, the method increases thedetectability and mapping efficiency of the desired objects by aniterative process comprising at least two steps: contrasting (i.e.texture enhancement) followed by object recognition.

[0041] Contrasting is performed by extracting several attributes frommultiple windows and feeding these to either a supervised, or anunsupervised neural network. The size, shape and direction of theextraction windows as well as the attributes are chosen in relation tothe objects we wish to detect. The windows may have a fixed shape anddirection, or they have data adaptive forms. In the latter case theyfollow the local dip and azimuth of the seismic events. The resultingoutput is a texture enhanced volume, which can be interpreted manually,or used as input to the object recognition phase.

[0042] Seismic attributes and supervised and unsupervised neuralnetworks have become increasingly popular in recent years in the realmof quantitative interpretation. The present invention extends the use ofthese techniques to seismic object detection. Moreover, the concept ofdirectivity is introduced in the attribute extraction process.

[0043] Directive seismic source arrays have been used for may years toattenuate unwanted signals hence increasing the contrast between desiredand unwanted energy. Since seismic acquisition must record all desiredenergy the source directivity is generally weak. Also in processing theconcept of directivity is used to increase the contrast between objectsand their background. Also these directivity processes are weak sincethey should not attenuate energy from seismic objects of interest.

[0044] In this method seismic object is improved by: focusing on oneclass of objects only; using directivity to extract the attributes; andthe use of neural networks to recombine the extracted attributes intonew attributes with improved separation power. The target can berelections, faults, chimneys, seismic anomalies or any other object ofinterest. The seismic texture, the spatial extension and orientation ofeach of these objects is different. Differences are both due to theseismic response and how the data has been handled in acquisition andprocessing.

[0045] To detect seismic objects requires knowledge about texture, size,shape and direction of the objects. One must ask which is characteristicof a fault, chimney or seismic anomaly in order to extract the bestattributes. These attributes are then recombined into even betterattributes via neural network mapping so that the objects can bedetected in an optimal way. For example, faults are in general dippingmore steeply then reflectors and the seismic response changes fasteralong fault planes than along reflectors. Since fast spatial variationsare mostly degraded by inaccuracies in acquisition and processing weknow that reflectors usually contain higher temporal frequencies thanfault images.

[0046] Seismic chimneys on the other hand appear as vertically degradedzones in the seismic image. These zones can completely mask thereflection energy from the sedimentary sequence.

[0047] Other examples of seismic objects and their characteristics are:Direct Hydrocarbon Indicators (DHI) and stratigraphic units. A DHI is aseismic anomaly, which is often characterized by a horizontal component,a change in amplitude and phase and a termination against otherreflectors. A stratigraphic unit can have many different responses.Usually the response changes along the reflecting unit, due to changesin rock and fluid parameters. Detecting these changes and relating theseto geological/petrophysical variations is the subject of seismicreservoir characterization. However, if the general response of aparticular unit differs from the surrounding reflectors, thisinformation can be used in an alternative auto-tracking scheme.

[0048] Once the decision is made which objects are to be detected anintelligent selection is made of attributes that have potential toincrease the contrast. Attributes can be amplitude, energy, similarity,frequency, phase, dip, azimuth etc. Moreover, attributes can beextracted (and merged) from different input cubes e.g. near—and faroffset stack, inverted Acoustic Impendance etc. The attributes are madedirective by the shape and orientation of the extraction window. Inchimney detection for example three vertically oriented extractionvolumes can be used to reflect that we are looking for verticallyoriented bodies of considerable dimensions. Knowledge about thecharacteristics of chimneys is used by calculating in each extractionvolume such attributes as energy and various types of trace-to-tractsimilarity.

[0049] In fault detection, static, vertically oriented calculationvolumes can also be used. To prevent non-vertical faults from “fallingout of” the extraction volume(s) the vertical directivity can bereduced. Reducing the vertical extension and increasing the horizontalextension of the extraction volumes does this.

[0050] To detect reflectors the calculation volumes may be orientedhorizontally. Again since reflectors are not perfectly horizontal thedirectivity may be reduced.

[0051] Generally the extraction volumes are either cubes or cylinders.Other forms may perform better, especially in the case where the objectsdo not have a fixed direction. For example, to detect faults, energy isan important attribute. In the ideal, it is desirable to calculate theenergy in a 2D window along the fault plane. As the orientation of thefault plane is unknown the directivity is reduced e.g. by using a coneshape extraction volume to compute the energy attribute.

[0052] The ideal extraction volume follows the desired object at everyposition. This implies that the extraction volume has a flexible shape,which follows the local dip and azimuth of the data. The local dipazimuth can be calculated in may different ways. The inventors havefound that the calculated local dip and azimuth cannot only be used tosteer the attribute extraction volumes but it is also a perfect vehicleto remove random noise prior to attribute extraction processes.

[0053] After the selected attributes have been extracted at arepresentative set of data points these will be recombined into a newset of attributes to facilitate the detection process. In this step,supervised or unsupervised neural networks can be used. The maindifference between supervised and unsupervised learning approaches liesin the amount of a-priori information that is supplied. Supervisedlearning requires a representative set of examples to train the neuralnetwork. For example networks can be trained to find the (possiblenon-linear) relation between seismic response and rock property ofinterest. In this case the training set is constructed from real orsimulated well data. In unsupervised (or competitive learning)approaches, the aim is to find structure within the data and thusextract relevant properties, or features. The resulting data segments(patterns) still need to be interpreted. An example of this approach isthe popular waveform segmentation method whereby waveforms along aninterpreted horizon are segmented. The resulting patterns are theninterpreted in terms of facies- or fluid changes.

[0054] In the object detection method the same principles are used. Withunsupervised learning approaches, attributes related to the objects tobe detected are used. With supervised learning approach, not only aremeaningful attributes used but locations in the seismic cube are alsoidentified where examples of the class of objects to be detected arepresent. Seismic attributes are calculated at these positions as well asat control points outside the objects. The neural network is thentrained to classify the input location as falling inside or outside theobject. Application of the trained network yields the desired textureenhanced volume in which the desired objects can be detected moreeasily.

[0055] Edge detection algorithms and pattern recognition tools can thenbe applied to the texture enhanced volume to further improve thedetectability of the object. The concept of directivity can also beapplied in these processes.

[0056] The chimney cube is a new seismic entity. A chimney cube is a 3Dvolume of seismic data, which highlights vertical disturbances ofseismic signals. These disturbances are often associated with gaschimneys. The cube facilitates the difficult task of manualinterpretation of gas chimneys. It reveals information of thehydrocarbon history and fluid flow models. In other words the chimneycube may reveal where hydrocarbons originated, how they migrated into aprospect and how they spilled from this prospect. As such a chimney cubecan be seen as a new indirect hydrocarbon indicator tool.

[0057] Chimney interpretation is also used in geo hazard evaluation.Correlating chimneys with mapped shallow gas indicators may confirm thepresence of shallow gas. As chimneys are signs of partially degradeddata, the cube can also be used as a quality control tool in processingand in the evaluation of attribute and depth maps.

[0058] Finally the cube can be used in determining acquisitionparameters. For example the success of 4C seismic depends on the abilityto undershoot gas, hence it depends on the interpretation of chimneys.

[0059] The chimney cube whose interpretation will be described below wascreated as follows:

[0060] 1. A seed interpretation was made with locations inside manuallyinterpreted chimneys and in a control set outside the chimneys.

[0061] 2. At the seed locations various energy and similarity attributeswere extracted in three vertically aligned extraction volumes around thelocations (directivity principle).

[0062] 3. Step 1 and 2 were repeated to create and independent test set.

[0063] 4. A fully connected Multi-Layer-Perceptron type of neuralnetwork was trained to classify the attributes into two classesrepresenting chimney or non-chimney (output vectors 1,0 or 1,0).

[0064] 5. The trained network was applied to the entire data setyielding outputs at each sample location. As the outputs arecomplementary we passed only the output on the chimney node to producethe final result: a cube with values between approx. 0 (no-chimney) andI (chimney).

[0065] Thus, a semi-automated method of detection of seismic objects isprovided. The method, which has wide applicability, is seismicprocessing and interpretation preferably includes:

[0066] 1. Focussing on one class of objects at the time.

[0067] 2. Extraction of attributes with potential to increase thecontrast between desired object and the background.

[0068] 3. The use of directivity in the attribute extraction process.

[0069] 4. The use of supervised and unsupervised neural networks torecombine the attributes into new attributes with improved separationpower.

[0070] 5. The possibility to iterate the process by first enhancing thetexture of the objects then detecting them by either manualinterpretation, or automated detection after application of edgedetection and pattern recognition algorithms.

[0071] A specific application of the method is chimney cube. This cubemay add a new dimension to seismic interpretation as an indirecthydrocarbon detector.

[0072] The mapping of seismic chimneys can be important in explorationas hydrocarbon indicators. The chimneys indicate present or previousvertical migration of fluids containing hydrocarbons, and can indicatemovement of hydrocarbons between different geological sequences. Thereare seismic indications that vertical migration of hydrocarbons appearperiodically. The mapping of chimneys at different levels may help tounderstand the hydrocarbon migration history, the migration routebetween a source rock and shallower prospects, as well as migration ofhydrocarbons between prospects, as well as migration of hydrocarbonsbetween prospects and shallower sediments.

[0073] As the upward migrating hydrocarbons may charge any shallowerreservoir, the mapping of chimneys also has significance in shallow gashazards evaluations for drilling.

[0074] Escape of fluids or gas through the seabed may generatenon-favorable conditions for seabed installations, like pockmarks, andseabed instability. The mapping of shallow chimneys is thereforeimportant in field development projects.

[0075] In the past, CO₂ resulting from petroleum production, has beenreinjected to the underground to prevent the release of CO₂ to the air.The mapping of possible chimneys is in such a case important to find asuitable injection location with low risk of CO₂ migration to theseabed, as well as in time-lapse seismic analysis for monitoring ofpossible CO₂ migration to the seabed during and after injection.

[0076] To better identify chimneys, seismic attributes which increasethe contrast between chimneys and the surroundings are used. Theamplitude values within chimneys are, in the majority of cases, observedto be low, as compared to the surroundings. Likewise, the seismic tracesimilarity is observed to be low within chimneys. Attributes that can beused to increase contrast between chimneys and the surroundings areamplitude, energy, trace correlations, tract similarity etc. Thedifferent attributes are input to a neural network which is trained todo a classification into chimneys and non chimneys. The verticalextension of chimneys is used as a criterion in the classification. Aschimneys appear as vertical disturbances in seismic data, all verticaldisturbances with the same seismic characteristics will be enhanced.

[0077] The final product is a cube where chimneys have been quantifiedby assigning maximum values (high probability) to the samples within thechimneys and minimum values (low probability) to the samples within thesurrounding volume.

[0078] Similar principles can be used to identify and quantify faultplanes and reflectors. The final cubes can be loaded into any standardinterpretation or mapping system for visualization like a standardseismic cube. The method may be applied on 2D as well as 3D data.

BRIEF DESCRIPTION OF THE DRAWINGS

[0079] The invention may be carried into practice in various ways andsome embodiments will now be described by way of example with referenceto the accompanying drawings, in which:

[0080]FIG. 1 is an outline procedure of a method in accordance with theinvention;

[0081]FIG. 2a is a schematic representation of extraction cubes within aseismic control volume for the study of an inclined object; and

[0082]FIG. 2b is a view similar to FIG. 2a for a gas chimney.

DETAILED DESCRIPTION OF THE DRAWINGS

[0083]FIG. 1 shows schematically a preferred system in accordance withthe invention. The procedure has effectively two steps. In the firststep, a seismic volume is defined and in dependence upon the nature ofwhat is known to be likely to be present, the appropriate attributes areselected. These are processed using control volumes within the seismicvolume which are tailored in their shape and directionality to suit thegeological feature or body which is to be studied. This results in anenhanced texture of the body; a texture enhanced volume.

[0084] In the second step, shape and geometrical constraints areapplied, again using the known directionality. This results in anenhanced separation; an object enhanced volume.

[0085] The process is then repeated on successive seismic volumes. Theentire process can also be repeated after the elapse of a significanttime interval. In this way, the development of a body, such as areservoir can be monitored.

[0086] The effectiveness of directional attributes can be demonstratedwith two examples. The first is horizon based, the second isthree-dimensional.

EXAMPLE I

[0087] Attributes extracted around a horizon are in principledirectional attributes. In conventional processing the orientation ofthe 3-D cube used is not changed, however, in the present invention,directionality is used to locate the extraction control volume. Foroptimal use of directivity the orientation of the control volume must beadapted to the local conditions. FIG. 2a shows this principle. Inpractice, the top and bottom of the extraction cube shown as the controlvolume would follow the horizon; the extraction cube is not in fact acube, nor a rectangle but a flexible body with tops and bottoms parallelto the horizon. This same concept is valid for the generalized 3-D casewhere the extraction bodies follow the surface that is defined from acentral extraction point.

[0088] The difference between ‘conventional’ and ‘true’ directivity foran attribute that expresses the similarity between trace segmentssurrounding an extraction point can be shown by computing the similarityin a time gate of −40 to +40 ms. In the conventional case, theorientation of the extraction cube is constant, in the true directivitycase, the orientation follows the horizon and results in an enhanceddefinition of the object.

EXAMPLE II

[0089] When gas seeps through the subsurface, it may leave a high gassaturation trail which may show up on seismic data as a chimney.Detection of chimneys is important from a drilling safety perspective.Also, from an exploration point of view, there may be a need to detectgas chimneys.

[0090] On seismic data, gas chimneys show up as vertical disturbances.Within the chimney, the energy decreases as does the trace-to-tracesimilarity (coherency). The shape of the chimney may vary considerably.Some are cylindrical (above a mound). Others are elongated or curved(along fractures and faults).

[0091] In this example a neural network is used to learn to recognizechimneys from a representative set of data points which are eitherinside, or outside a chimney. Input to the network is the inline numberplus a set of directional attributes extracted in three 80 ms timegates. The direction is vertical, so the three time gates are locatedabove (−120,−40), around (−40,+40) and below (+40,+120) each extractionpoint. In each gate, the energy of the central trace is computedtogether with 4 multi-trace attributes which express the similaritybetween traces surrounding the central trace. The desired output is 1for a chimney and 0 for a non-chimney. The trained network is applied tothe entire seismic volume yielding a new control volume in which thetexture of chimneys has been enhanced, in this case, expressed on ascale from 0 (no chimney) to 1 (chimney). Chimneys appear in differentshapes. Shape information can now therefore be utilized, e.g. viaspatial filters and/or shape detection techniques to further improve thechimney detection.

1. A method of processing acquired seismic data which comprisesextracting seismic information from the acquired data in a directionalong the spatial direction of a body of interest thereby producingdirectional seismic attributes.
 2. The method as claimed in claim 1,characterized in that the seismic information is extracted from acontrol volume, where the control volume has a size, shape andorientation determined by a typical size, shape and orientation of thebody of interest.
 3. The method as claimed in claim 1, characterized inthat the directional seismic attributes are used to enhance a texture ofthe body of interest.
 4. The method as claimed in claim 3, characterizedin that the texture enhancement step is the first step of a two stepprocedure, and the second step comprises a post-processing procedureapplied to the enhanced texture volume in a direction along the spatialdirection of the body of interest.
 5. The method as claimed in claim 4,characterized in that the second step comprises applying geometricalconstraints to the enhanced texture volume of the body of interest. 6.The method as claimed in claim 1, characterized in that the procedure isapplied to multiple seismic volumes in an iterative manner.
 7. Themethod is claimed in claim 6, characterized by extracting attributes inmultiple volumes on each side of an extraction point.
 8. The method asclaimed in claim, 3 characterized in that when enhanced texture volumesare recognized they are extracted and displayed for characterization. 9.The method as claimed in claim 1, characterized in that one or morespatial filters are used to increase the signal to noise ratio of thedata.
 10. The method as claimed in claim 9, characterized in that thelocal direction at every seismic sample position is used to adapt theorientation of the spatial filters.
 11. The method as claimed in claim1, characterized in that the directional attributes are combined in anintelligent way to enhance the difference between a particular body ofinterest and background.
 12. The method as claimed in claim 11,characterized in that the directional attributes are combined using anunsupervised learning approach.
 13. The method as claimed in claim 10,characterized in that the unsupervised learning approach is used toanalyze the internal structure of the acquired seismic data.
 14. Themethod as claimed in claim 13, characterized in that an algorithm usedsegments the data into a series of data segments, each segmentcontaining a combination of attributes for subsequent interpretation.15. The method as claimed in claim 1, characterized in that, after aprobable body of interest is detected, the size and shape of a controlvolume is varied during extraction to find a control volume to give amaximal contrast between attributes calculated over the control volumeand the same attributes calculated outside the control volume.
 16. Themethod as claimed in claim 1, characterized by the use of edge detectionalgorithms to establish boundaries of bodies with similarcharacteristics.
 17. The method as claimed in claim 1 for mapping agenerally vertical feature, such as a fault or a gas chimney, whichcomprise extracting seismic information from data acquired usinggenerally vertically orientated seismic control volumes sequentially inthe region of the vertical feature.
 18. The method as claimed in claim1, characterized by the use of time lapse seismic techniques in order todetect changes in shape over time of a feature, such as a reservoir.